Download INTERNATIONAL COMPETITION, RETURNS TO SKILL AND LABOUR MARKET ADJUSTMENT February 19, 2009

INTERNATIONAL COMPETITION, RETURNS TO SKILL AND
LABOUR MARKET ADJUSTMENT
Rod Falvey*, David Greenaway* and Joana Silva**
*School of Economics and GEP, University of Nottingham.
** World Bank and GEP, University of Nottingham.
February 19, 2009
Abstract
Does increased import competition lead to higher returns to skill within an industry and,
therefore, to greater incentives for skill acquisition? Does it also induce skill upgrading by the
industry’s existing workforce? To answer these questions we follow individual workers
across skills/occupations, firms and industries using a longitudinal matched employeremployee dataset covering virtually all workers and firms in Portugal over the 1986-2000
period. To identify the effects of international competition we use two exogenous measures of
changes in international competition at the industry-level. First, a quasi-natural experiment
based on the strong appreciation of the Portuguese currency in 1989-1992 period and preexisting differences in trade exposure across industries in a differences-in-differences
estimation. Second, source weighted real exchange rates defined at the industry-level. Based
on both empirical strategies, and on two different skill definitions, we show that international
competition increases returns to skill and induces skill/occupation-upgrading within an
industry.
Keywords: International trade, Skill acquisition, Labour market adjustment.
JEL Classification: F11, F16, J31, J62.
Address for correspondence: School of Economics, Sir Clive Granger Building, University of
Nottingham, Nottingham, NG7 2RD. Tel + 44 (0)115 9515469, Fax: + 44 (0)115 9515552. E-mail:
[email protected]. We are grateful to participants at the Midwest International
economics Meetings and IZA/World Bank Conference. Helpful comments on an earlier draft were
received from Carl Davidson, Steve Matusz, Richard Upward and Richard Kneller. Falvey and
Greenaway acknowledge financial support from the Leverhulme Trust under Programme Grant
F/00/114/AM. Silva acknowledges financial support from Fundação para a Ciência e a Tecnologia
Grant SFRH/BD/13162/2003 and Economic and Social Research Council Grant PTA-026-27-1258.
The finding, interpretations, and conclusions expressed in this paper are entirely those of the authors
and do not necessarily reflect the views of the World Bank.
1
1. Introduction
In recent decades the increased integration of national product markets has stimulated a large
literature aiming to identify and explain its labour market consequences. Among the
consequences of particular interest are adjustments in the market returns to different
skills/occupations and changes in workers skill-acquisition decisions. The former were the
early focus of this literature, in particular whether trade liberalisation was an important driver
of the increased wage inequality between skilled and unskilled workers observed in many
high-income countries. The general consensus emerging from this literature seems to be that
trade liberalization contributed to increase the skill premium, but played a small role relative
to skilled biased technological change (Slaughter 2000, Acemoglu 2002, Machin 2003).1
Other recent contributions argue that organisational change (Caroli and Van Reenen, 2001;
Black and Lynch, 2004; Garicano and Rossi-Hansberg, 2006) and declining unionisation
(Machin, 1997; Card, 2001) have also played an important role.
Recent work by Guadalupe (2007) reports a previously unnoticed driver of the increase in
returns to skill: changes in the degree of product market competition. Theoretically, she
shows that if product markets are imperfectly competitive, fiercer competition increases the
sensitivity of profits with respect to production costs. Provided that skilled workers are more
productive than unskilled workers, this induces a rise in demand for skills, which translates
into higher returns. The causal relationship between product market competition and returns
to skill is then empirically confirmed by individual panel data for the UK.
In this paper we note that by increasing returns to skill fiercer international competition will
also raise the incentives for skill acquisition. Indeed, recent theoretical work by Falvey et al.
(2007) shows that a production or price shock that increases relative wages of skilled workers
induces significant skill acquisition by the existing workforce.2 Understanding the
implications of increased competition for skill acquisition is important given the theoretical
possibility of poverty traps generated by lack of education and occupational choice (Barham
et al. 1995; Banerjee and Newman, 1993), and the role of human capital accumulation in
growth. To date, however, this relationship has not been subject to empirical scrutiny.
Furthermore, we note that by reducing monopoly rents increased competition may also
1
An important exception to this consensus is Wood (1998). For recent surveys of literature on globalization and
inequality see Greenaway and Nelson (2002), Feenstra and Hanson (2003), Bardhan (2005) and Goldberg and
Pavcnik (2007).
2
Modelling worker transitions induced by a trade shock is also the focus of Davidson and Matusz (2000, 2002 and
2004) and Long et al. (2007). However, whereas the first focuses on consequences of industry specific human
capital for the adjustment process, the second focuses on firm specific human capital.
2
generate wage differentials between sectors (Krueger and Summer, 1988), and thereby
increase workers’ incentives to switch industry.
Our paper makes two contributions. We begin by providing further evidence on the impact of
within-industry changes in competition on returns to skill using longitudinal matched
employee-employer data for Portugal. The data we use cover virtually all workers and firms
in the private sector over the 1986-2000 period, and are supplemented with industry-level
information on imports by the country of origin. The worker and firm dimensions of our data
are particularly important for our purposes, as they allow us to account for the role of firm
characteristics and composition effects.3 Our identification strategy involves two exogenous
measures of changes in international competition. First, following Cuñat and Guadalupe
(2005) and Guadalupe (2007), we exploit a strong appreciation of the Portuguese currency in
1989-1992 (over 25%) and pre-existing differences in cross-industry trade exposure in a
differences-in-differences estimation. Second, following Revenga (1992), Campa and
Goldberg (2001) and Bertrand (2004), we make use of industry-specific real exchange rates,
for which we have data for a later period (1991-2000). Based on both strategies, and on two
different skill definitions, we find strong confirmation for the hypothesis that within-industry
increases in international competition are an important determinant of rising wage inequality.
The second, and perhaps most important, contribution of this paper is to investigate whether
changes in international competition also cause skill upgrading and industry switching. Using
the aforementioned empirical strategies to identify exogenous changes in foreign competition,
we estimate transition probabilities between skills and/or industries (controlling for worker,
firm and sector characteristics) in a bivariate probit specification. We distinguish between
four alternatives: no change; moving industry; skill upgrading; moving industry and skill
upgrading. Our results provide strong support to the view that labour market adjustment to
globalisation involves significant worker movements across sectors and skills. Specifically,
we find that increased international competition induces skill acquisition, decreases skill
downgrading, and induces workers to move industry.
The remainder of the paper is organised as follows. In Section 2 we outline the empirical
methodology. In Section 3 we describe the data. Section 4 presents some descriptive statistics
about workers’ transitions. Section 5 discusses the empirical estimates, and Section 6
concludes.
3
See Abowd and Kramarz (1999) and Abowd et al (2006) for a detailed discussion of the effects of the
exclusion of these effects on earnings regressions.
3
3. Empirical Strategy
3.1. The effect of increased international competition on the returns to skill
Our analysis consists of two parts. First, we investigate whether increased import competition
leads to higher returns to skill within an industry and, therefore, incentive for skill acquisition.
Second, whether it also induces skill-upgrading by the industry’s existing workforce.
In order to argue for a causal effect of international competition, we identify two exogenous
changes in international competition at the industry level. First, we use a quasi-natural
experiment consisting on the strong appreciation of the Portuguese currency in 1989-1992 and
pre-existing differences in trade exposure across industries in a differences-in-differences
estimation. Second, we make use industry level of source-weighted real exchange rates at the
industry level where the weights are given by the import shares of different countries in the
base period (1990). Appreciation of the home currency leads to increased international
competition for national firms for two reasons: it reduces the prices that foreign competitors
can offer in the home country market; and it encourages more entry by increasing the number
of potential foreign firms that can sell in the home country. As argued by Revenga (1992),
Bertrand (2004) and Guadalupe (2007), exchange rate movements are largely unpredicted and
exogenous to the behaviour of firms and workers within each industry.
The first identification strategy has been employed by Guadalupe (2007) using UK data at the
worker-industry level focusing on the effect of international competition on returns to skills.
The data use is worker-industry level, therefore abstracting from potential effects of firm
heterogeneity (e.g. in compensation and retention policies) on workers earnings and
compositional effects that may arise from workers switching across firms. In this paper by
exploiting the worker-firm-industry level of detail of the Portuguese data we are able to
account for these effects by controlling for firm characteristics and include worker-firm fixed
effects. This is important as whereas in individual fixed-effects models the effect is identified
out of individuals who stay in the same firm as well as individuals who move after a shock, in
firm-worker fixed-effects models the effect is identified out of variation over the time period
in which the worker is employed in a given firm, thereby ensuring that unobserved changes to
the industry composition of employment are not driving the results4. The richness of our data
also allows us to use a more informative definition of skill level. In Guadalupe’s study skills
evaluation was exclusively based on workers’ occupations; our data permits evaluation based
4
See Abowd and Kramarz (1999) and Abowd et al (2006).
4
on occupations and schooling levels. Given the focus of our analysis on foreign competition
induced skill/occupation upgrading and its underlying incentives (returns to skills), it is
particularly important to investigate whether results are robust to these different measures.
Figure 1 depicts the evolution of the real effective exchange rate against all currencies
weighted by the respective country’s relative importance in Portuguese imports. As this
Figure makes clear, the Escudo appreciated sharply in 1989-1992, preceded and followed by a
period of relative stability.
Figure 1: Real effective exchange rate, Portuguese Escudo (1985=100)
130
125
120
115
110
105
100
95
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
90
Source: Bank of Portugal.
In Figure 2 we plot the difference in mean log wages between skilled and unskilled workers
of the manufacturing sector over our sample period. Whether we identify skill by schooling
only or using the ILO’s skill definition, between 1989 and 1992 this difference increased
sharply.
5
Figure 2: High to low skill wage differentials of the manufacturing sector
Skilled/Unskilled Log Wage Differential
1.25
1.2
1.15
1.1
1.05
1
0.95
0.9
0.85
1999
1998
1997
1996
1995
1994
1993
1992
1991
1989
1988
1987
1986
0.8
ln(wage if skill>3)-ln(wage if skill<3)
ln(wage if schooling>12)-ln(wage if schooling<=12)
Source: Author calculations from Quadros de Pessoal .
We use the pre- and post-appreciation period (1986-1988 and 1989-1992 respectively) and
pre-appreciation differences between sectors in trade openness to identify exogenous changes
in international competition in a difference in differences specification. The advantage of this
is that it controls for pre-existing differences across industries and changes common to all
industries. The hypothesis is that the appreciation represents a higher increase in the degree of
product market competition in the sectors that are (ex-ante) more open (treatment group)
relative to those fairly closed (control group). More specifically, we estimate:
ln wit = (indop88K * post 89 * skillit )λ + θ (indop88K * post 89 ) + ( post 89t * skillit )ς +
(indop88K * skillit )ϕ + skillit β + Xitα + ZJtν + γ hhiKt + φij + τ K + µt + Ψ it
(1)
where wit is the wage of worker i at year t; indop88 K is the degree of openness to trade of
industry K 5 and post89 is a dummy variable that takes the value zero in the preappreciation period (1986-1988) and one during the appreciation (1989-1992), skill it is a
vector comprising measures of the skill level of worker i at year t; X it is a vector of individual
characteristics (age, age squared, gender, tenure), Z Jt is a vector of characteristics of firm J
at year t (size, labour productivity, age, proportion of foreign owned capital, regional dummy)
5
Defined as the ratio of total industry trade (imports plus exports) to domestic demand (industry sales
plus net imports).
6
and φij , τ K and µ t are, respectively, worker-firm, industry and time fixed effects6; and Ψ it
is an exogenous disturbance. Note that since trade openness may vary endogenously with real
appreciation, the variable indop88 is computed as the level of openness for 1988. In this
specification λ is the differences-in-differences estimate of returns to skill and captures how
these vary with international competition. To get this estimate it is necessary to control for
differences in returns to skill before and after the experiment (captured by ς ) and differences
in returns to skill between sectors with different degrees of openness (captured by ϕ ).
The second identification strategy uses a more direct measure of exogenous change in
competition: changes in the weighted average of the log real exchange rates of importing
countries, where the weights are the shares of each trade partner in the industry’s total imports
in a base period. This index varies across industries based on the composition of imports by
country of origin. To avoid potential endogeneity issues, the weights should be prior to the
period under analysis. Given that the first years for which bilateral data on imports by
industry is available are 1990 and 1991, the period under analysis is restricted to 1991-2000.
This strategy was established in the literature by Revenga (1992) to investigate the effect of
international competition on industry wages and employment in US manufacturing, and has
been recently adopted by Campa and Goldberg (2001), Bertrand (2004) and Cuñat and
Guadalupe (2006) to examine the effect of competition on, respectively, industry wages and
employment, the sensitivity of wages to the unemployment rate, and provision of incentives
to top managers inside the firm. Using this identification strategy we estimate the following
equation:
ln wit = ICKt * skillit λ + θ ICKt + skillit β +
X itα + Z Jtν + γ hhiKt + +φij + τ K + µt + Ψ it
(2)
where ICKt is the source-weighted real exchange rate of industry J at time t and the
other variables are as above7. The main parameter of interest is λ , which reflects how returns
to skill vary with international competition and we expect it to be positive.
We consider two different specifications of equations (1) and (2). In the first, the worker’s
level of skill is based on schooling, defined as the number of completed years of education
(equation (2.1)). In the second, skill is computed using the International Labour Office’s
(ILO) correspondence Table between major groups of occupations in the 1988 International
6
Note that the model does not have a variable measuring openness on its own, nor a before and after dummy on its
own because they are swept out by the industry and time dummies.
7
See Appendix A for a detailed description of how the variables are constructed.
7
Standard Classification of Occupations (ISCO-88) and skill level (equation (2.2)). The ISCO88 “is based on the nature of the skills required to carry out the tasks and duties of the job not
the way these skills are acquired” (Hoffmann 2000, pp. 2), considering formal education,
training and experience. Table 1 presents the correspondence Table and description of
requirements associated with each skill level provided by Elias et al (1999)8. In the
econometric analysis we aggregate skill groups 1 and 2 as low skilled workers, which is then
used as the base category in the estimation of returns to skill.
Table 1: Definition of skill groups – ISCO 1988
Skill Level
4th
ISCO-88 Major Groups Included
1. Legislators, senior officials and managers;
2. Professionals.
Decription
Normally requires a degree or an
equivalent period of relevant work
experience.
3rd
3. Technicians and associate professionals.
Requires a body of knowledge
associated with a period of postcompulsory education but not to
degree level.
2nd
4. Clerks;
5. Service workers and shop and market sales
workers;
6. Skilled agriculture and fishery workers;
7. Craft and related workers;
8. Plant and machine operators and assemblers;
9. Elementary Occupations.
Requires knowledge as for first skill
level, but in addition typically have
a longer period of worker-related
training or work experience.
1st
Competence associated with general
education usually acquired by
completion of compulsory
education.
Source: International Labour Office (1990, pp. 2-3) and Elias et al. (1999) as in Upward and Wright (2007).
2.2. Increased international competition on skill acquisition and industry
relocation
Krueger and Summer (1988) show that by decreasing monopoly rents, and therefore the
ability of firms to pay higher wages, international competition may also generates wage
differentials between sectors. Increased competition can therefore increase returns to skill
(tested in this paper) and generates wage differentials between sectors for workers with the
same skill. While higher returns increase the incentives for skill acquisition, inter-sectoral
wage differentials increase the incentive to switch industry. To disentangle the effect
8
Using information for each worker schooling and tenure we have also checked the consistency of the described
educational requirements with those in our data for each skill group and our results are in line with the Elias et al.
description.
8
increased international competition on skill acquisition from that of industry relocation we
estimate a bivariate probit model. The underlying assumptions are that moving skill and
moving industry are two alternative routes for adjusting to increased competition, but they are
related.
We adopt the following baseline two equation system:
⎧⎪moveindit* = X itα 0 + θ 0 ∆ICKt + Z Jtν 0 + γ hhiKt + τ K + µt + Ψ it
⎨
*
⎪⎩ skillupit = X itα1 + θ1∆ICKt + Z Jtν 1 + γ hhiKt + τ K + µt + Λ it
(3)
where moveindit and skillupit are two observed binary indicator variables driven by the two
equation system of latent propensities to move industry ( moveindit* ) and skill upgrading
( skillupit* ). ∆ICKt is the percentage change in competition in industry K . Ψ it and Λ it are
exogenous disturbances (assumed to be correlated). The other variables are as before. The
observability criteria for the two sets of observed binary outcomes are
*
⎪⎧moveindit = 1(moveindit > 0)
⎨
*
⎪⎩ skillupit = 1(moveskillit > 0)
moveindit is equal to one if individual i in the subsequent observation is employed in a
different industry and skillupit is equal to one if individual i in the subsequent observation
has a higher level of skill. The main parameters of interest are θ 0 and θ 1 which reflect how
the propensities to move industry and skill vary with competition and we expect them to be
positive.
3. Data Description and Descriptive Statistics
3.1. The data set
Our data is from a longitudinal matched employer-employee dataset, “Quadros de Pessoal”
[QP], collected by the Portuguese Ministry of Employment, which covers virtually all
workers and firms in the Portuguese private sector over the 1986-2000 period, around
200,000 firms and more than 2 million workers each year. It provides comprehensive
information on worker’s demographic characteristics (age, gender, schooling), occupation
characteristics (occupational group, professional category, wage, hours worked) and plant
tenure, along with employing firm ID codes. Firm-level characteristics includes sales, number
of employees, equity, percentage of foreign capital, geographical location and date of
constitution, along with industry code. This code allows us to merge this data set with very
detailed trade data, disaggregated at the industry level with imports by country of origin,
9
collected by the Portuguese National Statistics Office (INE). Trade data is only available for
manufacturing for the period 1988-2000. As the Portuguese classification of industries has
been revised in 1994 to match the NACE-Rev 2, a concordance was needed and the analysis
considers 74 manufacturing industries9.
There are two important characteristics that guarantee this data set’s coverage and reliability.
First, data is collected from a compulsory administrative census that the Ministry of
Employment runs annually to check the firm’s compliance with labour law. Second, and
unique to this data base, firms are required by law to make the information provided to the
Ministry available to every worker in a public place of the establishment. Another important
advantage is its longitudinal nature that results from the fact that a unique identification
number is attributed to each worker the first time he enters the data set. This is based on his
social security number that does not change through time.
To control for any coding errors, we have performed extensive checks to guarantee the
accuracy of the worker data using information on gender, date of birth and maximum
schooling level achieved, as described in Appendix B. After these checks, we kept for
analysis full-time wage earners, aged between 16 and 65, earning at least the national
minimum wage, working in firms operating in mainland Portugal. The final panel used in the
analysis contains information on 2,998,727 workers, 467,269 firms, imports and exports from
74 manufacturing industries over the period 1986-2000, yielding a total of 15,613,149
worker-year observations.
3.2. Workers Transitions: Variable Creation
An industry (skill) switcher is an individual that in the subsequent observation is employed in
a different industry (has a different skill). The set of worker transitions contains five
alternatives: the worker changes industry retaining the same skill; moves up the skill ladder
while remaining in the same industry; moves down the skill ladder while remaining in the
same industry; moves up the skill ladder and moves industry; moves down the skill ladder and
moves industry. Skill classification follows the ILO’s skill definition described in Section 2.1
and industry classification follows the NACE-Rev. 2 at the 2-digit level.
9
The concordance used can be found at www.ine.pt/Prodserv/nomenclaturas/CAE.hml. Note that 56 industries
have direct equivalents in the old and new classifications and are, therefore, defined at the 2-digit level of the
NACE-Rev. 2. The remaining 46 categories of NACE-Rev. 2 do not have direct equivalents in the old
classification and are aggregated into 18 sectors.
10
3.3. Descriptive Statistics
Moving industry and/or skill appear to be important forms of labour reallocation. Around 17
per cent of all worker-year observations are of some kind of switching. Figure 3 provides
some detail on (average) rates of switching (expressed as a percentage of total number of
observations). Switching by changing both industry and skill is least common, on average
around 3 per cent of all worker-year observations are of this type. Switching in one dimension
alone (either skill or industry) is more common. The rate of industry switching conditional on
retaining the same skill is similar to that of changing skill conditional on staying in the same
industry, on average around 7 per cent of all worker-year observations are of each of these
types. Considering each of these forms of reallocation independently the (unconditional)
switching rate raises to around 10 per cent (either for industry or skill).
As Figure 3 makes clear, moving up the skill ladder is more common than moving down, but
both movements are important – the share of all workers who are upgraders and downgraders
is 9 and 3.8 per cent, respectively. The difference is driven by differences in the fraction that
moves skill whilst staying in the same industry. Whereas the unconditional rate of industry
switching and its components are similar in both cases, the average rate of skill upgrading,
conditional on staying in the same industry, is almost twice as much as that of skill
downgrading conditional on staying in the same industry.10
Figure 3: Rates of switching, 1991-2000
18%
16%
14%
12%
10%
8%
6%
4%
2%
0%
17.0%
14.6%
7.3%
8.6%
2.8%
8.7%
1.5%
6.9%
Move industry or skill
Diff. skill & same ind.
Diff. ind.& no skill upgrade
10
12.5%
4.5%
1.3%
2.4%
Move industry or skill
upgrade
Move industry or skill
downgrade
Diff. skill & diff. Ind.
Diff. ind.& no skill downgrade
Diff. ind.& same skill
Detailed tables of transition patterns across industries are available from the authors on request.
11
4. Estimation Results
4.1. Summary Statistics
Table 2 reports summary statistics for the main variables of interest over the 1986-200011
period. As can be seen, there is considerable variability of actual wage between industries.
Moreover, worker transitions between skills and occupations are an important feature of the
Portuguese labour market. In manufacturing the annual average share of workers moving
industry is 10 percent and skill-upgrading 6 percent. Also significant is the change in the real
exchange rate (the annual average change is 3 percent) and level of openness (the annual
average level is 37 percent). Three other characteristics of Portuguese labour markers are
worth noting: high female participation, low level of education and low share of population
in very skilled occupations.
Table 2: Summary Statistics, 1986-2000
Variable
11.57
Standard
Deviation
0.44
Move ind
0.10
0.30
Move skill
0.10
0.30
Move skill-up
0.06
0.24
Move skill-down
0.01
0.11
Stayer
0.83
0.38
Real exchange rate
1.37
0.66
% change real exchange rate
0.03
0.67
Openness to trade in 88
0.37
0.27
35.69
10.83
Actual Wage (ln)
Age
Age-square
Mean
1391.04
829.70
T enure
9.97
8.80
Male
0.59
0.49
Schooling
5.52
3.01
Skilled (Schooling>12)
0.02
0.14
1st Skill Level
0.17
0.37
2nd Skill level
0.75
0.43
3rd Skill Level
0.06
0.24
4th Skill Level
0.02
0.15
840.27
1425.81
Firm size (ln)
4.88
1.64
Firm Average Labour productivity
8.83
1.18
24.24
21.15
Fraction of Foreign Capital
0.11
0.30
Number observations
5,561,687
Herfindal Index
Firm Age
11
Note that 1986 is the first year for which the QP dataset is available and the appreciation finishes in 1992.
Baldwin (1988) and Dixit (1989) argue that the appreciation may permanently reshape the competitive structure of
the product market and therefore the analysis should be restricted to the end of the episode. Moreover, there has
been a recession in the Portuguese economy in 1993 that might contaminate the differences in differences estimate.
The second strategy requires industry bilateral trade data prior to the period under analysis. This is only available
from 1990.
12
4.2. International competition and the returns to skill
Table 3 presents the estimated results of equation (1) using the 1989-1992 appreciation as the
exogenous change in international competition. Columns 1-4 present estimates for skill
defined as the number of completed years of education, columns 5-8 report estimates for skill
using the ILO’s skill definition. For each, we start by estimating the difference results
(columns 1-2 and 5-6) and proceed by estimating difference in differences results (columns 34 and 7-8). The dependent variable in each column is the log real hourly wage. Each
regression includes regional and year dummies to control for disparities in the returns to skill
across regions and macro-shocks, respectively. To control for unobservable industry
characteristics we include a full set of industry-dummies: 74 industry fixed-effects.
Additionally, we run each specification with individual fixed effects (where the effect is
identified out of the within sector variation in competition) and proceed by estimating the
models with spell (work-firm) fixed effects. Whereas in the first case identification comes
from the within sector variation in competition, in the second it comes from within spell
variation, that is, from variation over the period in which the worker is employed in a given
firm. This procedure is important as we are interested in estimating the effect of within
industry changes in international competition on relative wages.
The coefficient on the interaction of skill variables with indop88* post 89 is always positive
and highly significant in all specifications. In addition, one can see that the difference in
difference estimates of returns to schooling (columns 3-4) and high skill (columns 7-8) are
lower than the difference estimates (columns 1-2 and 5-6, respectively) and that the
coefficients associated with the two-way interaction of the skill variables with, inter alia,
indop88 and post 89 are statistically significant. This confirms the importance of
accounting for the fact that more open sectors may systematically pay a lower skill premium
to start with and that during 1989-1992 returns to skill increased throughout the economy.
Controlling for these, results on column 3-4 and 7-8 show that, relative to the control group,
the appreciation had a positive and significant effect on the skill premium of the treatment
group, than in industries that are relatively shielded. In particular, the estimated coefficients in
column 8 indicate that for an industry with average trade openness (0.37), the preappreciation return to skill was 4 percent and the post-appreciation return, 15.4 percent. The
full effect of the appreciation is only captured by the estimated coefficient on the interaction
between the skill and the experiment variable (0.024 on column 8), which indicates that for an
industry with average exposure to trade in 1988, the effect of the appreciation was to increase
the differential (in returns to skill) by 0.88 percent (relative to an industry with no trade prior
to 1988).
13
Table 4 also presents estimated results of equation (2) identifying exogenous changes as
source-weighted industry real exchange rate movements. Columns 1-2 present the estimates
for skill defined as the number of completed years of education, columns 3-4 report estimates
using the ILO’s skill definition. Column 1 and 3 present the individual fixed effects estimates
and column 2 and 4 the spell (worker-firm) fixed effects estimates. A full set of industry, year
and regional dummies is included in all specifications. The coefficient on the interaction of
skill variables with the exchange rate index is positive and highly significant in all
specifications, confirming that when the exchange rate index increases the impact of skill
acquisition on actual wage is higher. Note that, as pointed out by Bertrand (2004), this
methodology, by simultaneously including individual fixed effects and industry dummies,
estimates the relationship between changes (not absolute values) in international competition
and returns to skill within industries.
Our results are therefore very much in line with Guadalupe (2007). This is particularly
reassuring given that firm characteristics and work-firm fixed effects are highly statistically
significant. Based on two different identification strategies and skill definitions, we find
strong confirmation of the hypothesis that increased international competition is an important
determinant of rising wage inequality between skilled and unskilled workers.
14
Table 3: Effect of international competition on return to skill: 1989-1992 appreciation
(1)
Variable
age
age-square
tenure
indop88*post89
schooling
indop88*post89*schooling
0.617
(172)***
-0.217
(50.8)***
-0.014
(13.4)***
-0.184
(92.6)***
0.005
(9)***
0.022
(77.1)***
indop88*schooling
post89*schooling
mskill
hskill
indop88*post89*mskill
indop88*post89*hskill
indop88*mskill
indop88*hskill
post89*mskill
post89*hkill
hhi
firmsize
firmlabprod
firmage
%foreignK
-0.025
(26.9)***
0.038
(66.8)***
0.006
(37.1)***
-0.005
(10.7)***
-0.003
(2.2)**
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.619
0.497
0.502
0.656
0.665
0.652
0.654
(83.2)*** (122)*** (65.9)*** (184)***
(90)*** (184)***
(90)***
-0.188
-0.185
-0.159
-0.274
-0.241
-0.282
-0.248
(41.8)*** (43.1)*** (35.2)*** (64.4)*** (53.9)*** (66.4)*** (55.6)***
-0.001
-0.014
-0.002
-0.014
-0.001
-0.014
-0.001
(0.6) (13.7)***
(0.9) (13.5)***
(0.4) (13.6)***
(0.5)
-0.174
-0.083
-0.085
-0.082
-0.080
-0.073
-0.072
(85.1)*** (31.5)*** (31.8)*** (61.7)*** (57.8)*** (55.6)*** (52.4)***
0.002
0.004
0.002
(3.4)*** (5.4)*** (3.2)***
0.020
0.002
0.003
(71.6)*** (4.5)*** (5.6)***
-0.004
-0.007
(2.9)*** (4.9)***
0.012
0.010
(52.8)*** (45.9)***
0.030
0.022
0.022
0.017
(16.2)*** (11.6)*** (6.7)*** (5.2)***
0.089
0.073
0.050
0.049
(20.5)*** (17.3)*** (6.6)*** (6.5)***
0.142
0.128
0.041
0.036
(42.6)*** (38.3)*** (6.6)*** (5.6)***
0.223
0.202
0.022
0.024
(31.3)*** (29.2)***
(1.7)*
(1.9)*
-0.029
-0.033
(4.1)*** (4.7)***
0.010
-0.023
(0.6)
(1.4)
0.067
0.060
(23.3)*** (20.6)***
0.118
0.105
(19.4)*** (17.5)***
-0.025
-0.023
-0.023
-0.028
-0.028
-0.027
-0.027
(27)*** (24.2)*** (24.6)*** (29.7)*** (29.6)*** (28.7)*** (28.6)***
0.066
0.038
0.066
0.038
0.065
0.038
0.065
(60.7)*** (67.2)*** (60.9)*** (66.2)*** (59.4)*** (66.4)*** (59.7)***
0.006
0.006
0.005
0.006
0.006
0.006
0.005
(33.7)*** (36.4)*** (33.1)*** (37.5)*** (33.8)*** (36.9)*** (33.3)***
-0.047
-0.005
-0.033
-0.004
-0.056
-0.004
-0.050
(7.7)*** (10.6)*** (5.5)*** (10.4)*** (9.4)*** (10.4)*** (8.4)***
-0.022
-0.003
-0.021
-0.004
-0.023
-0.004
-0.023
(14.9)***
(1.8)* (14.3)*** (3.1)***
(16)***
(3)*** (15.7)***
Year+industry dummies
yes
yes
yes
yes
yes
yes
yes
yes
Regional dummies
yes
yes
yes
yes
yes
yes
yes
yes
Worker fixed effects
yes
yes
yes
yes
Worker-firm fixed effects
yes
yes
yes
yes
Obs.
2,300,567
R-square
0.189
0.147
0.215
0.164 0.185
0.144
0.187
0.147
* significant at 10%; ** significant at 5%; *** significant at 1%. The period of analysis is 1986-1992. Absolute value of t-statistic
in parentheses, based on robust standard errors clustered by individual. The variables age, tenure are divided by 10, and age-square
and hhi by 1000. Dependent variable: log real hourly wage.
15
Table 4: Effect of international competition on returns to skill: exchange rate changes
Variable
age
age-square
tenure
exchrate
schooling
exchrate*schooling
(1)
(2)
(3)
(4)
0.411
(156.32)***
-0.256
(83.7)***
0.014
(17.91)***
-0.119
(59.31)***
0.001
(2.22)**
0.006
(24.65)***
-0.241
(74.78)***
0.004
(2.26)**
-0.110
(54.38)***
-0.002
(2.98)***
0.007
(28.29)***
-0.266
(87.67)***
0.013
(16.06)***
-0.089
(55.60)***
-0.252
(78.38)***
0.004
(2.16)**
-0.077
(46.59)***
0.021
(8.36)***
0.034
(7.23)***
0.018
(10.86)***
0.039
(12.03)***
0.005
(1.79)*
0.037
(89.15)***
0.005
(31.57)***
-0.007
(23.22)***
0.032
(30.55)***
0.009
(3.71)***
0.012
(2.59)***
0.017
(10.42)***
0.038
(12.02)***
0.008
(3.26)***
0.048
(66.39)***
0.004
(24.7)***
0.131
(9.32)***
0.009
(8.48)***
mskill
hskill
exchrate*mskill
exchrate*hskill
hhi
firmsize
firmlabprod
firmage
%foreignK
0.005
(1.82)*
0.037
(88.81)***
0.005
(31.58)***
-0.007
(23.12)***
0.031
(30.45)***
0.008
(3.24)***
0.048
(66.38)***
0.004
(24.60)***
0.133
(9.44)***
0.009
(8.58)***
Year+industry dummies
yes
yes
yes
yes
Regional dummies
yes
yes
yes
yes
Worker fixed effects
yes
yes
Worker-firm fixed effects
yes
yes
3,858,747
Obs.
R-square
0.226
0.005
0.001
0.003
* significant at 10%; ** significant at 5%; *** significant at 1%. The period of analysis is
1991-2000. Absolute value of t-statistic in parentheses, based on robust standard errors
clustered by individual. The variables age, tenure are divided by 10, and age-square and
hhi by 1000. Dependent variable: log real hourly wage.
4.3. International competition and skill upgrading
Using bivariate probit models, Table 5 reports the results for the two equation systems ((3.1)
and (3.2)) presenting each of the different forms of relocation that may be induced by
increased competition. The marginal effects on the probability of each potential outcome
(when all variables are held at their mean) are presented12. All regressions include industry
fixed effects, year and regional dummies and standard errors are clustered by individuals (to
12
Regression results can be found in Appendix C.
16
account for the fact that the individual error term may be autocorrelated). Columns 1-5
present estimates for the latent propensities of moving industry ( moveind * ) and moving skill
( moveskill * ), columns 6-10 report the estimates for the latent propensities of moving
industry ( moveind * ) and skill-upgrading ( skillup* ). Columns 1-3 present each of the
different forms of reaction to increased competition against the probability of no change in
skill or industry, Pr( moveindiJKt = 0, moveskilliJKt = 0 X it , ∆ICKt , Z Jt , hhiKt ) , that is:
Pr(moveindiJKt = 1, moveskilliJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr(moveindiJKt = 1, moveskilliJKt = 0 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr(moveindiJKt = 0, moveskilliJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) , respectively.
In addition, Columns 4-5 present bivariate probit estimates of the marginal effects of each of
the covariates on the following unconditional probabilities:
Pr(moveind iJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr(moveskilliJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) , respectively.
Similarly, Columns 6-8 present each of the different forms of reaction to increased
competition against the probability of no skill-upgrading or switching industry,
Pr(moveindiJKt = 0, skillupiJKt = 0 X it , ∆ICKt , Z Jt , hhiKt ) , that is:
Pr(moveindiJKt = 1, skillupiJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr(moveindiJKt = 1, skillupiJKt = 0 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr(moveindiJKt = 0, skillupiJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) , respectively.
In addition, Columns 6-10 present bivariate probit estimates of the marginal effects of each of
the covariates on the following unconditional probabilities:
Pr(moveind iJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) ,
Pr( skillupiJKt = 1 X it , ∆ICKt , Z Jt , hhiKt ) , respectively.
The marginal effect of interest on the variable indop88* apprec is always positive and
highly significant. Results therefore indicate that the propensity to switch (industry and/or
skill) in response to the appreciation is higher in sectors more open to trade. In addition,
results in column 1-6 show that the effect of increased competition on skill acquisition and
industry relocation is similar (columns 5 and 6). However, the effect on the probability of
changing in one dimension only is stronger than the effect on the probability of changing both
skill and industry (coefficient in columns 2-3 is higher than in 1). Furthermore, results on the
17
second specification (column 6-10), show that increased international competition in the
1989-1992 appreciation also increases significantly the propensity to skill-upgrade, whether
remaining in the same industry or changing industry, though the effect on the latter is weaker.
In particular, the estimated coefficients on trade exposure, the post-appreciation dummy and
their interaction (columns 9) indicate that for an industry with average trade exposure in 1988
(0.37), appreciation increased the propensity to move industry by 0.23 percent and, this effect
was 0.05 percent higher than in an industry with 10 percentage point lower trade exposure in
1988. Similarly, column 10 indicates that for an industry with average trade exposure in 1988
(0.37), appreciation increased the propensity to skill-upgrade by 0.1 percent and, this effect
was 0.03 percent stronger than in an industry with 10 percentage point lower trade exposure
in 1988.
In addition, all signs of the coefficients of the control variables are as expected. Worker
covariates indicate that moving between skills and industries is significantly less frequent in
older workers but the effect of an additional year of age is higher in younger than older
workers. High tenured workers also switch industry and/or skill less frequently. Skilled
workers tend to be more mobile than unskilled workers. Male workers move more frequently
than female workers. Moreover, industry covariates show that a high degree of industry
concentration is associated with higher switching rates. Finally, firm covariates indicate that
elevated firm size, labour productivity and participation of foreign capital predict lower
switching rates, whereas firm’s age increases this rate.
Table 6 presents bivariate probit estimates using source-weighted industry real exchange rate
fluctuations to identify changes in competition13. Results are in line with those previously
obtained and confirm the association between increased international competition and
additional skill acquisition and industry relocation. For a worker who is average on all
characteristics, employed in a firm that is average in all characteristics that belongs to an
industry that has the average level of the Herfindahl-Hirschman index, a 1 percentage point
increase in the exchange rate index is associated with a probability to move industry 0.03
percentage points higher and probability to skill-upgrading by 0.02 percentage points higher.
The effect on skill upgrading while remaining in the same industry is more than twice as large
as the effect on skill upgrading and moving industry simultaneously.
13
Regression results can be found in Appendix C.
18
Table 5: Marginal effects of international competition on the probability of skill-upgrading and/or moving industry: 1989-1992 appreciation
Move industry (ind.) and/or move skill
Propensity to move
Propensity to Propensity to
move ind.
move skill
ind.&skill
ind.only
skill only
Predicted Propensity
Variables
age
(1)
2.63%
(2)
5.95%
-0.036
(83.5)***
0.032
(57.95)***
-0.014
(106.36)***
0.032
(32.15)***
0.002
(4.6)***
0.002
(1.35)
-0.003
(10.64)***
0.007
(39.63)***
0.006
(11.8)***
-0.002
(43.46)***
-0.002
(31.5)***
0.001
(13.92)***
-0.006
(20.17)***
0.007
(7.36)***
-0.027
(21.97)***
-0.023
(88.18)***
0.011
(8.54)***
0.003
(3.16)***
0.004
(1.25)
-0.001
(1.36)
0.009
(26.08)***
0.006
(5.52)***
-0.005
(39.99)***
-0.005
(41.26)***
0.002
(21.12)***
-0.023
(36.4)***
(3)
6.71%
(4)
8.58%
(5)
Move industry (ind.) and/or move skill-up
Propensity to Move
Propensity to Propensity to
move ind.
skill-up
ind.&skill-up
ind. only
skill-up only
(6)
9.35%
1.15%
(7)
7.43%
(8)
4.23%
(9)
8.58%
(10)
5.38%
-0.111
-0.029
-0.146
-0.024
-0.003
-0.096
-0.027
-0.120
(106.66)***
(23.51)***
(111.49)***
(101.24)*** (2.82)***
(131.61)***
(21.98)***
(136.2)***
age-square
0.124
0.005
0.156
0.023
-0.021
0.107
0.002
0.130
(90.19)***
(2.98)***
(90.07)***
(78.55)*** (14.34)***
(113.19)***
(1.5) (113.97)***
tenure
-0.014
-0.036
-0.027
-0.008
-0.028
-0.016
-0.037
-0.024
(42.74)***
(107.3)***
(66.65)***
(112.25)*** (93.45)***
(69.67)***
(107.06)***
(86.25)***
skilled
0.065
0.043
0.097
0.019
0.023
0.049
0.042
0.068
(30.26)***
(21.51)***
(35.2)***
(31.56)*** (14.39)***
(31.68)***
(20.99)***
(34.66)***
indop88*post89
0.003
0.005
0.005
0.001
0.004
0.002
0.005
0.003
(2.25)**
(3.96)***
(3.23)***
(4.57)*** (3.59)***
(2.33)**
(4.01)***
(2.99)***
indop88
0.002
0.006
0.004
0.001
0.004
0.002
0.005
0.003
(0.53)
(1.36)
(0.89)
(1.37)
(1.16)
(0.69)
(1.25)
(0.91)
post89
-0.007
-0.004
-0.010
-0.003
-0.001
-0.010
-0.004
-0.012
(11.31)***
(4.66)***
(12.24)***
(17.21)***
(1.48)
(18.79)***
(4.52)***
(19.38)***
male
0.009
0.016
0.015
0.003
0.012
0.007
0.016
0.011
(21.04)***
(33.91)***
(28.49)***
(41)*** (29.93)***
(24.25)***
(34)***
(29.59)***
hhi
0.010
0.012
0.016
0.003
0.010
0.005
0.013
0.007
(8.42)***
(8.17)***
(10.4)***
(9.39)*** (7.72)***
(4.63)***
(8.62)***
(5.96)***
firmsize
-0.002
-0.007
-0.004
-0.001
-0.006
0.000
-0.007
-0.001
(12.61)***
(45.81)***
(23.4)***
(31.14)*** (45.06)***
(0.28)
(45.72)***
(7.24)***
firmlabprod
0.000
-0.007
-0.001
-0.001
-0.006
0.001
-0.007
0.000
(2.07)**
(42.46)***
(8.22)***
(22.53)*** (43.87)***
(6.93)***
(43.43)***
(0.52)
firmage
0.000
0.002
0.000
0.000
0.002
0.000
0.002
0.000
(3.07)***
(21.14)***
(1.8)*
(10.11)*** (22.2)***
(5.73)***
(21.59)***
(2.31)**
%foreignK
0.009
-0.029
0.003
-0.002
-0.027
0.008
-0.029
0.006
(12.91)***
(34.55)***
(3.63)***
(13.67)*** (36.65)***
(15.95)***
(34.92)***
(9.69)***
Observations
2,300,567
2,300,567
rho
0.475
0.299
%Correctely predicted
0.999
0.999
McFadden's pseudo R2
0.052
0.066
* significant at 10%; ** significant at 5%; *** significant at 1%. The period of analysis is 1986-1992. Absolute value of t-statistic in parentheses, based on robust standard errors
clustered by individual. All specifications include a full set of industry dummies (74 industry fixed-effects), year and regional dummies. Marginal effects are computed at the mean of
each variable. The variables age, tenure are divided by 10, and age-square and hhi by 1000.
19
Table 6: Marginal effect of international competition on the probability of skill-upgrading and/or moving industry: exchange rate fluctuations
Predicted Propensity
Variables
age
Move industry (ind.) and/or move skill
Propensity to move
Propensity Propensity to
to stay
move ind.
ind.&skill
ind. only
skill only
(1)
(2)
(3)
(4)
(5)
2.10%
6.18%
6.47%
85.24%
8.29%
Propensity to
move skill
(6)
8.58%
Move industry (ind.) and/or move skill-up
Propensity to Move
Propensity Propensity to Propensity to
skill-up
to stay
move ind.
ind.&skill-up
ind. only
skill-up only
(7)
(8)
(9)
(10)
(11)
(12)
0.95%
7.35%
3.9%
87.80%
8.30%
4.85%
-0.032
(115.61)***
0.032
(89.19)***
-0.009
(110.1)***
0.018
(40.42)***
0.008
(4.8)***
0.003
(29.78)***
0.000
(19.82)***
0.000
(8.28)***
-0.001
(19.21)***
0.000
(0.9)
-0.002
(11.07)***
-0.010
-0.100
0.142
-0.042
-0.132
-0.020
-0.021
-0.081
0.122
-0.041
-0.101
(13.6)*** (127.1)*** (121.14)***
(46.35)***
(135.92)***
(130.33)*** (24.94)***
(157.86)*** (119.04)***
(44.97)*** (165.53)***
age2
-0.001
0.112
-0.144
0.032
0.144
0.021
0.009
0.091
-0.121
0.030
0.112
(0.56) (106.18)***
(92.39)***
(26.4)***
(110.99)***
(107.02)***
(8.31)***
(133.44)***
(89.32)***
(25.03)*** (137.66)***
tenure
-0.016
-0.013
0.039
-0.025
-0.022
-0.005
-0.020
-0.013
0.039
-0.025
-0.019
(82.4)*** (55.19)*** (113.34)***
(102.07)***
(74.29)***
(118.02)*** (88.69)***
(80.47)*** (131.76)***
(102.08)***
(94.39)***
skilled
0.007
0.048
-0.072
0.024
0.065
0.008
0.016
0.023
-0.047
0.024
0.031
(8.38)*** (39.71)***
(46.99)***
(22.41)***
(44.2)***
(34.78)*** (16.64)***
(31.52)***
(37.15)***
(21.84)***
(34.56)***
∆exchrate
0.006
0.019
-0.033
0.027
0.014
0.007
0.021
0.018
-0.046
0.028
0.024
(4.53)***
(1.38)
(4.76)***
(5.01)***
(2.47)**
(7.21)***
(4.41)***
(5.01)***
(7.15)***
(5.21)***
(5.66)***
male
0.010
0.001
-0.014
0.013
0.005
0.002
0.011
0.002
-0.015
0.013
0.004
(33.9)***
(3.98)***
(29.23)***
(36.2)***
(11.15)***
(32.87)*** (34.85)***
(10.68)***
(37.1)***
(36.57)***
(16.13)***
hhi
0.001
0.000
-0.001
0.001
0.000
0.000
0.001
0.000
-0.001
0.001
0.000
(24.46)***
(0.03)
(19.54)***
(25.34)***
(5.58)***
(17.6)*** (24.32)***
(1.71)*
(22.13)***
(24.94)***
(5.12)***
firmsize
0.000
-0.001
0.001
0.000
-0.002
0.000
0.000
0.000
0.000
0.000
0.000
(1.6) (11.04)***
(8.44)***
(1.23)
(11.12)***
(1.6)
(1.84)*
(0.36)
(1.86)*
(1.92)*
(0.65)
firmlabprod
-0.004
0.001
0.004
-0.005
0.000
0.000
-0.005
0.001
0.004
-0.005
0.001
(33.86)***
(9.35)***
(18.64)***
(32.74)***
(2.22)**
(14.91)*** (34.88)***
(9.91)***
(23.65)***
(33.98)***
(5.06)***
firmage
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
(2.49)**
(1.14)
(0.85)
(2.28)**
(0.68)
(1.7)*
(2.97)***
(0.36)
(2.34)**
(2.96)***
(0.07)
%foreignK
-0.006
0.000
0.008
-0.008
-0.002
-0.001
-0.008
0.002
0.007
-0.008
0.001
(13.9)***
(0.09)
(10.88)***
(14.4)***
(2.98)***
(6.27)*** (14.97)***
(5.13)***
(10.02)***
(14.47)***
(2.87)***
Observations
3,858,747
3,858,747
rho
0.409
0.267
% correctely predicted
0.963
0.999
McFadden's pseudo R2
0.053
0.065
* significant at 10%; ** significant at 5%; *** significant at 1%. The period of analysis is 1991-2000. Absolute value of t-statistic in parentheses, based on robust standard errors clustered by individual. All
specifications include a full set of industry dummies (74 industry fixed-effects), year and regional dummies. Marginal effects are computed at the mean of each variable. The variables age, tenure are divided by 10,
and age-square and hhi by 1000.
20
6. Conclusions
This paper confirms international competition as a source of increased returns to skill. Using
a large panel (1986-2000) of matched employer-employee data for the Portuguese
manufacturing sector, two different skill definitions, and two different identification
strategies of exogenous changes in international competition, we show that returns to skill
within an industry increase with competition. Furthermore, we identify increasing
international competition as a significant determinant of skill upgrading. This result is valid
both when workers stay in the same industry and when they move. It indicates that skill
acquisition is an important part of the adjustment process to a policy change or shock
(exogenous to the wage setting conditions within the country) that changes relative wages,
and suggests that policy attention to the consequences of increased competition for human
capital accumulation seems merited.
21
Appendices
APPENDIX A: Variable description
Wage data is the log deflated monthly earnings actually received including the base wage,
tenure-related and other regularly paid components. Wages are deflated using the consumer
price index.
Worker covariates: Tenure is defined as the number of years with the same firm, schooling as
the number of completed years of education and skilled as having a number of completed
years of education higher than 12 (that marks the end of high-school in Portugal).
Sector covariates: The Herfindahl-Hirschman index of industrial concentration in industry K
is defined as
hhiK = ∑ (mshareJ ) 2 where mshare J is the market share of firm J of
J ∈K
industry K defined as firm sales over total industry sales. The level of openness for industry
K in 1988 (indop88) is defined as the ratio of total industry trade (imports plus exports) to
domestic demand (industry sales plus net imports).
Source weighted industry real exchange rate index: is defined as the weighted average of the
log real exchange rate of each of the importing countries. The weights are the shares of each
foreign country’s imports in industry total imports in a base period (1990-1991). Real
exchange rates are nominal exchange rates (expressed in foreign currency per escudo)
multiplied by the Portuguese consumer price index and divided by the foreign country
consumer price index. Nominal exchange rates have been provided by the Bank of Portugal
and foreign consumer prices index’s from the International Financial Statistics of the
International Monetary Fund.
Firm covariates: The firm size is defined as the number of employees, firm (nominal
average) labour productivity as the ratio of firm sales to number of employees, firm age as
current year minus year of creation of the firm, and percentage of foreign capital as share of
foreign capital in equity.
22
APPENDIX B: Consistency checks to the information on the employer-employee
dataset
We have performed a number of consistency checks on the information provided by
“Quadros de Pessoal” to guarantee the accuracy of the data used.
a) Elimination of invalid or duplicated worker identification codes in a given year:
According to the Ministry of Employment valid worker’s ID codes have 6 to 10 digits. In
each year, observations with codes with less than 6 or more than 10 digits were not
considered. Observations with duplicated identification codes were also eliminated. These
restrictions led to dropping an average 8.93 percent and 4.51 percent of the observations in
the original yearly datasets, respectively.
b) Consistency check of data for each worker across years: We have merged the yearly
information and identified inconsistencies when gender, date of birth or year of hiring (for the
same firm) were reported changing, or the highest schooling level was reported decreasing
(or was missing) over time for the same worker.
(b1) Correcting missing values when reported data for the rest of the period was absolutely
consistent by assigning the reported value for the remaining period. This changed 0 percent14,
2.25 percent, 0.71 percent and 0.23 percent of the observations in the initial panel for gender,
age, schooling and tenure firm, respectively.
(b2) Correcting inconsistent data across years by taking information reported over half of the
times as correct15. Inconsistent values on gender were replaced by the value reported over
half of the cases the worker has been observed, provided that the year of birth in that
observation is the same as that reported in more than 50 percent of the cases for that worker.
Similar procedures have been implemented for year of birth and schooling. This affected 0.87
percent, 1.77 percent, 6.65 percent and 1.68 percent of the initial panel for gender, year of
birth and schooling, respectively. The whole information on a worker has been dropped when
inconsistencies persisted after this correction. This restriction led to dropping 8.4 percent,
1.08 percent and 6.28 percent of the observations for gender, year of birth or schooling,
respectively.
14
15
Two observations.
Note that this is a more demanding criterion than simply using the modal value as replacement.
23
(b3) Dropping workers with remaining missing data on gender, age or schooling: 0 percent,
0.15 percent, 0.99 percent due to missing data on gender, age and schooling, respectively.
The checked panel included 22,686,298 worker-year observations and 3,525,485 workers.
Table B.1: Shares of regions and years
Variable
Region
North Coast
Central Coast
Lisbon
Inland
Algarve
Year
1986
1987
1988
1989
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Observations
% of Total
31.88
14.24
44.28
7.18
2.42
5.06
5.5
5.47
5.19
6.42
6.72
6.74
6.86
7.76
8.21
8.8
8.74
9.3
9.23
15,613,149
APPENDIX C: Regression results on the effect of international competition on
skill acquisition and industry relocation
The estimated results of the two equation systems ((4.1) and (4.2)) presented in Section 4.3
are marginal effects. Appendix Table C.1 displays the regression results instead.
24
Table C.1: Effect of international competition on skill acquisition and industry relocation: regression results
Stay in the same industry and skill as base
Propensity to
Propensity to Propensity to
Propensity to
move skill
move ind.
move skill
move ind.
(1)
(2)
(3)
(4)
Variables
age
age2
tenure
skilled
indop88*post89
indop88
post89
-0.184
(23.58)***
0.031
(2.98)***
-0.232
(105.24)***
0.238
(24.67)***
0.034
(3.96)***
0.038
(1.36)
-0.023
(4.66)***
-0.876
(111.2)***
0.934
(90.03)***
-0.163
(65.14)***
0.445
(43.59)***
0.028
(3.23)***
0.022
(0.89)
-0.061
(12.25)***
∆exchrate
male
hhi
firmsize
firmlabprod
firmage
%foreignK
Year+Regional dummies
Industry fixed effects
Observations
rho
McFadden's pseudo R2
0.102
(33.37)***
0.079
(8.17)***
-0.045
(45.97)***
-0.041
(42.39)***
0.014
(21.14)***
-0.183
(34.47)***
yes
yes
2,300,567
0.475
0.052
0.094
(28.09)***
0.098
(10.39)***
-0.025
(23.42)***
-0.009
(8.22)***
0.001
(1.8)*
0.019
(3.63)***
yes
yes
-0.275
(46.36)***
0.208
(26.41)***
-0.165
(100.59)***
0.145
(24.47)***
-0.844
(134.36)***
0.921
(110.12)***
-0.141
(73.01)***
0.338
(52.85)***
0.174
(5.01)***
0.085
(35.95)***
0.004
(25.37)***
-0.001
(1.23)
-0.031
(32.75)***
0.001
(2.28)**
-0.055
(14.38)***
yes
yes
3,858,747
0.409
0.053
0.087
(2.47)**
0.030
(11.12)***
0.001
(5.58)***
-0.010
(11.12)***
0.002
(2.22)**
0.000
(0.68)
-0.012
(2.98)***
yes
yes
Propensity to
move ind.
Stay same ind and no skill up as base
Propensity to
Propensity to Propensity to
skill-up
move ind.
skill-up
(5)
-0.172
(22.04)***
0.016
(1.5)
-0.233
(104.98)***
0.233
(24.02)***
0.035
(4.01)***
0.035
(1.25)
-0.023
(4.52)***
(6)
-1.094
(138.04)***
1.193
(115.15)***
-0.222
(82.35)***
0.446
(45.47)***
0.028
(2.99)***
0.024
(0.91)
-0.111
(19.41)***
0.103
(33.44)***
0.084
(8.62)***
-0.045
(45.87)***
-0.042
(43.32)***
0.015
(21.59)***
-0.186
(34.84)***
yes
yes
2,300,567
0.299
0.066
0.098
(29.05)***
0.067
(5.96)***
-0.008
(7.24)***
0.001
(0.52)
-0.002
(2.31)**
0.053
(9.69)***
yes
yes
(7)
-0.268
(44.98)***
0.198
(25.03)***
-0.166
(100.56)***
0.142
(23.81)***
(8)
-1.008
(164.66)***
1.114
(137.06)***
-0.187
(91.08)***
0.256
(41.27)***
0.181
(5.21)***
0.087
(36.31)***
0.004
(24.97)***
-0.002
(1.92)*
-0.032
(33.98)***
0.002
(2.96)***
-0.055
(14.46)***
yes
yes
3,858,747
0.267
0.066
0.243
(5.66)***
0.041
(16.05)***
0.001
(5.12)***
-0.001
(0.65)
0.006
(5.06)***
0.000
(0.07)
0.012
(2.87)***
yes
yes
* significant at 10%; ** significant at 5%; *** significant at 1%. The period of analysis is 1986-1992 in Column 1-2 and 5-6 and 1992-2000 in Column 3-4 and 7-8.
Absolute value of t-statistic in parentheses, based on robust standard errors clustered by individuals. The variables age, tenure are divided by 10, and age-square and hhi by
1000.
25
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